Video signals are often corrupted by noise during acquisition or transmission processes. Noise is a major source of degradation in picture quality. As TV screens get ever larger, video noise has become more annoying to viewers. Therefore, there has been a need for high quality noise reduction systems to improve video quality.
Traditional 2-dimensional (2D) noise reduction methods mainly involve linear processing (filtering) in either spatial or spectrum domains. Such noise reduction is based on attenuating high frequency signals components which represent noise. However, while reducing noise by attenuating the high frequency components, such linear processing removes some important image details and causes image edge blurring.
In order to prevent image edge blurring, noise reduction filtering needs to be adaptive to image local structures. One such adaptive technique is known as directional filtering wherein a directional filter is used to avoid image blurring by adapting to image edge directions in such a way that the filter is always applied along the edge direction, not across the edge direction.
Although the directional filter does prevent some image edge blurring, a drawback is that noise residuals in homogeneous image regions tend to cluster around the noise outliers (especially for high noise) due to the local spatial operation nature of the algorithm. This often leaves the processed image appearing “dirty ” in the homogeneous regions.